Transcript Testing current understanding of Amazon phenology using a Monte Carlo Markov Chain algorithm Silvia Caldararu (First year PhD) Paul Palmer, Drew Purves 1.

Testing current understanding of Amazon phenology
using a Monte Carlo Markov Chain algorithm
Silvia Caldararu (First year PhD)
Paul Palmer, Drew Purves
1. Introduction
Forests play an important role in the global carbon cycle through
photosynthesis and respiration, and through the emission of hydrocarbons.
To quantify forest carbon budgets and understand how they will respond to
changing environmental conditions, we need to understand the factors that
determine the temporal variations of vegetation. Ground-based data are
sparse in time and space, making it difficult to relate these data to larger
spatial and temporal scales.
Satellite observations of vegetation optical
properties allow us to observe vegetation
cover at global scales with sub-1km
resolution. These data are useful to fill in
gaps left by ground-based data, particularly
over remote tropical regions. Leaf area index
(LAI), the ratio of one-sided leaf area to the
underlying ground area, can be calculated
using a radiative algorithm based on the
fraction of light at different wavelengths
absorbed and reflected by vegetation.
2. The Amazon
Our initial focus is on the Amazon rain
forest, one of the largest tropical
forests in the world, where our
knowledge of phenology is incomplete
due to its large biodiversity (see
Figure).
The Amazon exhibits two seasons:
Monthly
1) a dry season (July to November) LAI from MODIS Terra satellite
maximum surface solar radiation
and 2) a wet season (DecemberMay); while May-June are
transition months. Longer dry
seasons occur over Eastern
Amazonia. Satellite observations
reveal large, as yet unexplained,
seasonal swings in LAI over the
Myneni et al, PNAS, 2006
Amazon rainforest (see Figure).
3. Modelling approach
The observed variation in LAI can be described as the difference between
the number of leaf layers lost and the number added:
dLAI
 gain (t )  loss (t ) LAI
dt
Leaf gain and loss for any vegetation type will be limited by a number of
environmental factors, but mainly temperature, sunlight, water availability
and soil fertility. The deep root system of the Amazonian rainforest gives
plants access to the deeper soil layers, which are not water depleted during
the dry season. We have developed a simple phenology model that can be
fitted to available data to test hypotheses about environmental factors.
4. Testing the LAI model
To accurately fit a model to a large data set, as in
the case of the global-scale space-borne LAI data,
there is a need for an efficient algorithm. We use
the Metropolis algorithm, also known as simulated
annealing, which uses a Monte Carlo Markov Chain
(MCMC) approach to explore model parameter
space.
How does it work?
Step 1: Choose a
starting point
Step 2: Make the
jump
(a1, b1, c1)
(a0, b0, c0)
Step 3: Calculate the likelihood L for both
points.
Step 4: Take the decision
If L1>L0: accept the new parameters
If L1<L0::
Accept the new
parameters with
probability P
Reject the new
parameters with
probability (1-P)
Step 5: Go back to step 2.
After convergence, the resulting posterior
distribution can be averaged to give the desired
parameter values.
5. Future work
•Can we accurately reproduce observed spacebased LAI data over the Amazon using a relatively
simple model description of driving environmental
factors?
•How will vegetation respond to possible changes
in climate such as the Amazon forest die-back as
predicted by the Hadley Centre climate model?
•Does the phenology over other tropical rainforests
behave in the same way, e.g., can the Amazon
model reproduce phenology over African
rainforests?
•What are the implications of our new phenology
model for understanding the size and location of
reactive and unreactive carbon fluxes?